HYPER-PARAMETER TUNING FOR FAIR CLASSIFICA-TION WITHOUT SENSITIVE ATTRIBUTE ACCESS Anonymous

Abstract

Fair machine learning methods seek to train models that balance model performance across demographic subgroups defined over sensitive attributes like race and gender. Although sensitive attributes are typically assumed to be known during training, they may not be available in practice due to privacy and other logistical concerns. Recent work has sought to train fair models without sensitive attributes on training data. However, these methods need extensive hyper-parameter tuning to achieve good results, and hence assume that sensitive attributes are known on validation data. However, this assumption too might not be practical. Here, we propose Antigone, a framework to train fair classifiers without access to sensitive attributes on either training or validation data. Instead, we generate pseudo sensitive attributes on the validation data by training a biased classifier and using the classifier's incorrectly (correctly) labeled examples as proxies for minority (majority) groups. Since fairness metrics like demographic parity, equal opportunity and subgroup accuracy can be estimated to within a proportionality constant even with noisy sensitive attribute information, we show theoretically and empirically that these proxy labels can be used to maximize fairness under average accuracy constraints. Key to our results is a principled approach to select the hyper-parameters of the biased classifier in a completely unsupervised fashion (meaning without access to ground truth sensitive attributes) that minimizes the gap between fairness estimated using noisy versus ground-truth sensitive labels.

1. INTRODUCTION

Deep neural networks have achieved state-of-the-art accuracy on many tasks including face recognition (Buolamwini & Gebru, 2018; Grother et al., 2010; Ngan & Grother, 2015) , autonomous driving (Zhang et al., 2021; Chitta et al., 2021) , medical image diagnosis (Litjens et al., 2017; Cheplygina et al., 2019 ), etc. But, prior work (Hovy & Søgaard, 2015; Oren et al., 2019; Hashimoto et al., 2018a) has found that state-of-the-art networks exhibit unintended biases towards specific population groups, especially harming minority groups. Seminal work by Buolamwini & Gebru (2018) demonstrated, for instance, that commercial face recognition systems had lower accuracy on darker skinned women than other groups. A body of work has sought to design fair machine learning algorithms that account for a model's performance on a per-group basis (Prost et al., 2019; Sagawa* et al., 2020; Liu et al., 2021; Sohoni et al., 2020) . Much of the prior work assume that demographic attributes like gender and race on which we seek to train a fair model, which we refer to as sensitive attributes, are available on training and validation data Sagawa* et al. (2020); Prost et al. (2019) . However, there is a growing body of literature (Veale & Binns, 2017; Holstein et al., 2019) highlighting many real-world settings in which sensitive attributes may not be available. This is for multiple reasons. For example, the data subject may abstain from providing sensitive information to eschew potential discrimination in future (Markos et al., 2017) . In other settings, the attributes on which the model discriminates might not even be known (Citron & Pasquale, 2014; Pasquale, 2015) . For instance, in algorithmic hiring decisions, Köchling & Wehner (2020) highlight that bias and discrimination are recognized only after making real world decisions on applicants due to unknown attributes on which the model discriminates during training. Consequently, a large American e-commerce company had to cease using algorithmic tools for hiring purposes as it was unintentionally discriminating female applicants (Dastin, 2018) .

